Each checkpoint is a torch.save dict with keys model, epoch and metric. The matching training configs are included here and in the code repository under configs/.

Usage

bash

pip install huggingface_hub
hf download hjh1037/FLAME flame_starcop.pt flame_emit.pt --local-dir .

See the code repository for evaluation and visualization instructions.

python

import torch, yaml
from flame.model import build_model

cfg = yaml.safe_load(open('configs/flame_starcop.yaml'))
model = build_model(cfg['model']).eval()
state = torch.load('flame_starcop.pt', map_location='cpu', weights_only=False)
model.load_state_dict(state['model'])

Citation

bibtex

@article{heo2026flame,
  title={FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery},
  author={Heo, Junhyuk and Park, Junhwan and Sim, Sancheol and Choi, Beomkyu and Cho, Woojin},
  journal={arXiv preprint arXiv:2606.01577},
  year={2026}
}